How to use the torchkbnufft.AdjMriSenseNufft function in torchkbnufft

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mmuckley / torchkbnufft / tests / test_kb_construction.py View on Github

table_oversamp=table_oversamp, kbwidth=kbwidth, order=order)
    ob = KbInterpBack(
        im_size=im_sz, grid_size=grid_sz, n_shift=n_shift, numpoints=numpoints,
        table_oversamp=table_oversamp, kbwidth=kbwidth, order=order)

    ob = KbNufft(
        im_size=im_sz, grid_size=grid_sz, n_shift=n_shift, numpoints=numpoints,
        table_oversamp=table_oversamp, kbwidth=kbwidth, order=order, norm=norm)
    ob = AdjKbNufft(
        im_size=im_sz, grid_size=grid_sz, n_shift=n_shift, numpoints=numpoints,
        table_oversamp=table_oversamp, kbwidth=kbwidth, order=order, norm=norm)

    ob = MriSenseNufft(
        smap=smap, im_size=im_sz, grid_size=grid_sz, n_shift=n_shift, numpoints=numpoints,
        table_oversamp=table_oversamp, kbwidth=kbwidth, order=order, norm=norm)
    ob = AdjMriSenseNufft(
        smap=smap, im_size=im_sz, grid_size=grid_sz, n_shift=n_shift, numpoints=numpoints,
        table_oversamp=table_oversamp, kbwidth=kbwidth, order=order, norm=norm)

    # test 2d tuple inputs
    im_sz = (256, 256)
    smap = torch.randn(*((1,) + im_sz))
    grid_sz = (512, 512)
    n_shift = (128, 128)
    numpoints = (6, 6)
    table_oversamp = (2**10, 2**10)
    kbwidth = (2.34, 2.34)
    order = (0, 0)
    norm = 'None'

    ob = KbInterpForw(
        im_size=im_sz, grid_size=grid_sz, n_shift=n_shift, numpoints=numpoints,

mmuckley / torchkbnufft / tests / test_pytorch_sparse_grad_adj_matching.py View on Github

x = params_2d['x']
    y = params_2d['y']
    ktraj = params_2d['ktraj']
    smap = params_2d['smap']

    for device in device_list:
        x = x.detach().to(dtype=dtype, device=device)
        y = y.detach().to(dtype=dtype, device=device)
        ktraj = ktraj.detach().to(dtype=dtype, device=device)

        sensenufft_ob = MriSenseNufft(
            smap=smap,
            im_size=im_size,
            numpoints=numpoints
        ).to(dtype=dtype, device=device)
        adjsensenufft_ob = AdjMriSenseNufft(
            smap=smap,
            im_size=im_size,
            numpoints=numpoints
        ).to(dtype=dtype, device=device)

        real_mat, imag_mat = precomp_sparse_mats(ktraj, sensenufft_ob)
        interp_mats = {
            'real_interp_mats': real_mat,
            'imag_interp_mats': imag_mat
        }

        y.requires_grad = True
        x = adjsensenufft_ob.forward(y, ktraj, interp_mats)

        ((x ** 2) / 2).sum().backward()
        y_grad = y.grad.clone().detach()

mmuckley / torchkbnufft / tests / test_kb_construction.py View on Github

table_oversamp=table_oversamp, kbwidth=kbwidth, order=order)
    ob = KbInterpBack(
        im_size=im_sz, grid_size=grid_sz, n_shift=n_shift, numpoints=numpoints,
        table_oversamp=table_oversamp, kbwidth=kbwidth, order=order)

    ob = KbNufft(
        im_size=im_sz, grid_size=grid_sz, n_shift=n_shift, numpoints=numpoints,
        table_oversamp=table_oversamp, kbwidth=kbwidth, order=order, norm=norm)
    ob = AdjKbNufft(
        im_size=im_sz, grid_size=grid_sz, n_shift=n_shift, numpoints=numpoints,
        table_oversamp=table_oversamp, kbwidth=kbwidth, order=order, norm=norm)

    ob = MriSenseNufft(
        smap=smap, im_size=im_sz, grid_size=grid_sz, n_shift=n_shift, numpoints=numpoints,
        table_oversamp=table_oversamp, kbwidth=kbwidth, order=order, norm=norm)
    ob = AdjMriSenseNufft(
        smap=smap, im_size=im_sz, grid_size=grid_sz, n_shift=n_shift, numpoints=numpoints,
        table_oversamp=table_oversamp, kbwidth=kbwidth, order=order, norm=norm)

    # test 3d tuple inputs
    im_sz = (10, 256, 256)
    smap = torch.randn(*((1,) + im_sz))
    grid_sz = (10, 512, 512)
    n_shift = (5, 128, 128)
    numpoints = (6, 6, 6)
    table_oversamp = (2**10, 2**10, 2**10)
    kbwidth = (2.34, 2.34, 2.34)
    order = (0, 0, 0)
    norm = 'None'

    ob = KbInterpForw(
        im_size=im_sz, grid_size=grid_sz, n_shift=n_shift, numpoints=numpoints,

mmuckley / torchkbnufft / tests / test_kb_construction.py View on Github

cur_table = KbNufft(im_sz, order=order, kbwidth=kbwidth).table
                check_tables(base_table, cur_table)

                cur_table = KbInterpBack(
                    im_sz, order=order, kbwidth=kbwidth).table
                check_tables(base_table, cur_table)

                cur_table = KbInterpForw(
                    im_sz, order=order, kbwidth=kbwidth).table
                check_tables(base_table, cur_table)

                cur_table = MriSenseNufft(
                    smap, im_sz, order=order, kbwidth=kbwidth).table
                check_tables(base_table, cur_table)

                cur_table = AdjMriSenseNufft(
                    smap, im_sz, order=order, kbwidth=kbwidth).table
                check_tables(base_table, cur_table)

mmuckley / torchkbnufft / tests / test_pytorch_sparse_grad_adj_matching.py View on Github

x = params_3d['x']
    y = params_3d['y']
    ktraj = params_3d['ktraj']
    smap = params_3d['smap']

    for device in device_list:
        x = x.detach().to(dtype=dtype, device=device)
        y = y.detach().to(dtype=dtype, device=device)
        ktraj = ktraj.detach().to(dtype=dtype, device=device)

        sensenufft_ob = MriSenseNufft(
            smap=smap,
            im_size=im_size,
            numpoints=numpoints
        ).to(dtype=dtype, device=device)
        adjsensenufft_ob = AdjMriSenseNufft(
            smap=smap,
            im_size=im_size,
            numpoints=numpoints
        ).to(dtype=dtype, device=device)

        real_mat, imag_mat = precomp_sparse_mats(ktraj, sensenufft_ob)
        interp_mats = {
            'real_interp_mats': real_mat,
            'imag_interp_mats': imag_mat
        }

        x.requires_grad = True
        y = sensenufft_ob.forward(x, ktraj, interp_mats)

        ((y ** 2) / 2).sum().backward()
        x_grad = x.grad.clone().detach()

mmuckley / torchkbnufft / tests / test_adjoints.py View on Github

y = params_2d['y']
    ktraj = params_2d['ktraj']
    smap = params_2d['smap']

    for device in device_list:
        x = x.detach().to(dtype=dtype, device=device)
        y = y.detach().to(dtype=dtype, device=device)
        ktraj = ktraj.detach().to(dtype=dtype, device=device)

        sensenufft_ob = MriSenseNufft(
            smap=smap,
            im_size=im_size,
            numpoints=numpoints,
            coilpack=True
        ).to(dtype=dtype, device=device)
        adjsensenufft_ob = AdjMriSenseNufft(
            smap=smap,
            im_size=im_size,
            numpoints=numpoints,
            coilpack=True
        ).to(dtype=dtype, device=device)

        x_forw = sensenufft_ob(x, ktraj)
        y_back = adjsensenufft_ob(y, ktraj)

        inprod1 = inner_product(y, x_forw, dim=2)
        inprod2 = inner_product(y_back, x, dim=2)

        assert torch.norm(inprod1 - inprod2) &lt; norm_tol

mmuckley / torchkbnufft / tests / test_pytorch_sparse_grad_adj_matching.py View on Github

y = params_2d['y']
    ktraj = params_2d['ktraj']
    smap = params_2d['smap']

    for device in device_list:
        x = x.detach().to(dtype=dtype, device=device)
        y = y.detach().to(dtype=dtype, device=device)
        ktraj = ktraj.detach().to(dtype=dtype, device=device)

        sensenufft_ob = MriSenseNufft(
            smap=smap,
            im_size=im_size,
            numpoints=numpoints,
            coilpack=True
        ).to(dtype=dtype, device=device)
        adjsensenufft_ob = AdjMriSenseNufft(
            smap=smap,
            im_size=im_size,
            numpoints=numpoints,
            coilpack=True
        ).to(dtype=dtype, device=device)

        real_mat, imag_mat = precomp_sparse_mats(ktraj, sensenufft_ob)
        interp_mats = {
            'real_interp_mats': real_mat,
            'imag_interp_mats': imag_mat
        }

        x.requires_grad = True
        y = sensenufft_ob.forward(x, ktraj, interp_mats)

        ((y ** 2) / 2).sum().backward()

mmuckley / torchkbnufft / tests / test_sparse_adjoints.py View on Github

y = params_2d['y']
    ktraj = params_2d['ktraj']
    smap = params_2d['smap']

    for device in device_list:
        x = x.detach().to(dtype=dtype, device=device)
        y = y.detach().to(dtype=dtype, device=device)
        ktraj = ktraj.detach().to(dtype=dtype, device=device)

        sensenufft_ob = MriSenseNufft(
            smap=smap,
            im_size=im_size,
            numpoints=numpoints,
            coilpack=True
        ).to(dtype=dtype, device=device)
        adjsensenufft_ob = AdjMriSenseNufft(
            smap=smap,
            im_size=im_size,
            numpoints=numpoints,
            coilpack=True
        ).to(dtype=dtype, device=device)

        real_mat, imag_mat = precomp_sparse_mats(ktraj, sensenufft_ob)
        interp_mats = {
            'real_interp_mats': real_mat,
            'imag_interp_mats': imag_mat
        }

        x_forw = sensenufft_ob(x, ktraj, interp_mats)
        y_back = adjsensenufft_ob(y, ktraj, interp_mats)

        inprod1 = inner_product(y, x_forw, dim=2)

mmuckley / torchkbnufft / tests / test_adjoints.py View on Github

x = params_2d['x']
    y = params_2d['y']
    ktraj = params_2d['ktraj']
    smap = params_2d['smap']

    for device in device_list:
        x = x.detach().to(dtype=dtype, device=device)
        y = y.detach().to(dtype=dtype, device=device)
        ktraj = ktraj.detach().to(dtype=dtype, device=device)

        sensenufft_ob = MriSenseNufft(
            smap=smap,
            im_size=im_size,
            numpoints=numpoints
        ).to(dtype=dtype, device=device)
        adjsensenufft_ob = AdjMriSenseNufft(
            smap=smap,
            im_size=im_size,
            numpoints=numpoints
        ).to(dtype=dtype, device=device)

        x_forw = sensenufft_ob(x, ktraj)
        y_back = adjsensenufft_ob(y, ktraj)

        inprod1 = inner_product(y, x_forw, dim=2)
        inprod2 = inner_product(y_back, x, dim=2)

        assert torch.norm(inprod1 - inprod2) &lt; norm_tol

mmuckley / torchkbnufft / profile_torchkbnufft.py View on Github

num_nuffts = 5
    else:
        dtype = torch.float
        if use_toep:
            num_nuffts = 50
        else:
            num_nuffts = 20
    cpudevice = torch.device('cpu')

    image = image.to(dtype=dtype)
    ktraj = ktraj.to(dtype=dtype)
    smap = smap.to(dtype=dtype)

    kbsense_ob = MriSenseNufft(smap=smap, im_size=im_size).to(
        dtype=dtype, device=device)
    adjkbsense_ob = AdjMriSenseNufft(
        smap=smap, im_size=im_size).to(dtype=dtype, device=device)

    adjkbnufft_ob = AdjKbNufft(im_size=im_size).to(dtype=dtype, device=device)

    # precompute toeplitz kernel if using toeplitz
    if use_toep:
        print('using toeplitz for forward/backward')
        kern = calc_toep_kernel(adjkbsense_ob, ktraj)
        toep_ob = ToepSenseNufft(smap=smap).to(dtype=dtype, device=device)

    # precompute the sparse interpolation matrices
    if sparse_mats_flag:
        print('using sparse interpolation matrices')
        real_mat, imag_mat = precomp_sparse_mats(ktraj, adjkbnufft_ob)
        interp_mats = {
            'real_interp_mats': real_mat,

How to use the torchkbnufft.AdjMriSenseNufft function in torchkbnufft

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